Generative adversarial networks[1]} were proposed by Goodfellow, based on game theory. In a pioneering work, C. Ledig et al[2]} used SRGAN to learn the mapping from LR to HR images in an end-to-end manner, achieving performance superior to that of previous work. A low-resolution image \(I^{L R}\)  is input to a generator network to generate the reconstructed image \(I^{SR}\) , while a discriminator network takes the high-resolution images \(I^{H R}\)  and \(I^{SR}\)  as input to determine which is the real image and which is the reconstructed image.
